Data corruption is one of the more common computer errors. Corruption typically occurs when code or other stored computer data is intentionally or unintentionally changed from its original, correct form. The errors resulting in data corruption may be introduced to the data during reading, writing, storage, processing, transmission, and the like, which can cause unintended changes to the data. The corruption can be systematic or random, and even a small change can fundamentally break a program, render a file useless, or even crash a system. Some of the causes for data corruption include infection by malware, improperly exiting a program, hardware malfunction, incorrectly written code, and the like. Oftentimes, however, the root cause of the data corruption in a productive (or production) system is unknown, at least initially. To further complicate the situation, it may be very difficult to reproduce the data corruption from the productive system in a test system due to the differences in code between the productive system and the test system, customization of the data in the productive system by different clients, previous manual data repairs performed on the data, and the like.
When data becomes corrupt, it is of paramount importance to fix the issue before further damage occurs. Ideally, it would be best to restore a clean backup copy of the data as it was before the corruption. However, restoring a backup of the data can be time consuming and results in downtime. Furthermore, in certain situations it is not possible to restore an old copy of the data, for example, if the corrupt data remains undetected for a significant period of time the backup data may be too old. In addition, copying the corrupt data to a test system might also be difficult if not impossible. In order to copy the data, all the related entries have to be identified. Additionally the code and the customizing of the code has to be exactly the same in both the productive system and the test system. Another option is to repair the productive data while it is active within the productive system. However, repairing the data correctly without allowing any further damages can be a challenging task. For example, error situations during install of complex changes could lead to time-consuming repairs. A common scenario is that one or more objects are copied to a lower release, while they contain functionality that is only available in higher releases. As a result, syntax errors may occur and the objects must be repaired and restored one by one. Also, locating an issue within a productive system can be time consuming due to modifications by a client. As a result, a productive system might be down for a significant period of time while the errors are identified and fixed.
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